{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook (JN) is meant to serve as a template for one way in which researchers can publish their data alongside their research findings. In addition to a JN like this one, the method of data publication for which we are advocating also involves the use of MyBinder. Because the UCLA Data Science Center is working to develop UCLA's instance of Dataverse, this workflow is intended for research data that is going to be ingested into UCLA's Dataverse instance[<sup>1</sup>](#fn1). As such, the three major components needed for this workflow are Jupyter Notebook (preferably accessed with a package manager like Anaconda), MyBinder, and Dataverse.\n",
    "\n",
    "By utilizing JN, we are able to break down code into manageable chunks, and we are also able to easily annotate, explain, or  format our code using Markdown. The end goal is for documents like this to be ingested into Dataverse alongside their respective dataset(s). This will add transparency and interoperability to publications that require, encourage, or permit the publication of research data in addition to the research findings themselves. It can also help to further explicate the methodology employed to analyze or work with the dataset(s), giving authors a way to more easily explain their thought process and workflow. Additionally, because Jupyter Notebook can be used by scholars to do actual research and not just as a way to publish data after the fact, it's possible for researchers to use this workflow without adding a significant amount to their workload.\n",
    "\n",
    "Which brings us to the final note about this document. The Gapminder data that we are working with is not part of a specific research project. This is appropriate in this instance because we are developing a workflow that involves using a brand new feature in Dataverse: integrating MyBinder and Jupyter Notebook so that users can run code for a given dataset directly from Dataverse. As such, it would not make sense to use \"live\" research data as fodder for experimentation, since reusing old data allows us the time and flexibility to thoroughly test our workflow for a variety of potential use cases. \n",
    "\n",
    "In a \"real\" use case, our JN would contain the code and accompanying markdown text that produced the results or findings of the researcher(s). In our case, because we lack research results or some kind of \"product\", we are instead doing some basic data cleaning and data visualization with the data. Additionally, over the course of developing this workflow we have identified two different approaches for writing this kind of JN. The first method, which we employed, is to provide explanations for an audience that has little to no experience with Python. The second approach is much more likely to be employed, which is to write the JN with just basic explanations, assuming your audience has at least intermediate experience with Python.\n",
    "\n",
    "Although the approach that we used is admittedly more work, in theory this work does not necessarily need to be done by the PI, for example. Instead, explicating code for a given dataset could be done by experts like the Data Science Center staff in the UCLA Library in exchange for partial authorship, for example. Regardless, there can exist a clear division of labor between the \"actual\" work that researchers are most interested in doing and this kind of work. As academic research increasingly revolves around one or more digital datasets, as well as the new digital research tools necessary to work with datasets, the need to provide supplemental documentation like this JN becomes more and more apparent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gapminder Data Visualization\n",
    "### Overview\n",
    "##### This Jupyter Notebook (JN) uses Python to visualize existing GDP data from the Gapminder Foundation. These visualizations rely on two Python modules, matplotlib and pandas. All code necessary to run these visualizations is contained below, including code that loads the two requisite Python modules for you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, we will load in the appropriate Python modules, matplotlib and pandas. The syntax below means that we can \"call\" or reference both matplotlib and pandas with the shorthand plt and pd, respectively. There is strong convention within the Python community to use plt and pd as the standard shorthand for their respective modules and we follow that convention here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now plot a very simple graph with some arbitrary data that we create. The arbitrary data below consists of two lists, \"time\" and \"position,\" each of which contains a list of four numbers. We plot these numbers in a simple graph to confirm that our matplotlib module is loaded and functioning.\n",
    "\n",
    ">*Note the syntax `plt.plot`, `plt.xlabel`, `plt.ylabel`, etc. This code is referencing the matplotlib module using our designated shorthand, plt.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Position (km)')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = [0, 1, 2, 3]\n",
    "position = [0, 100, 200, 300]\n",
    "\n",
    "plt.plot(time, position)\n",
    "plt.xlabel('Time (hr)')\n",
    "plt.ylabel('Position (km)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start working with the Gapminder data. Here we have included the code for loading the pandas module, even though we've already loaded it, to better illustrate some of the functionality of the pandas module. Pandas allows us to load a dataset into our Python program. In this case, we are loading up gapminder_gdp_oceania.csv and are assigning this dataset to the variable \"data\". This variable will function in much the same way as the shorthand we used to access matplotlib and pandas. Note that although variable names can be anything, it is considered good practice to use variable names that relate to, or in some way otherwise elicit, the dataset that you are assigning to the variable. Choosing helpful variable names will not only allow others to more easily read and understand your code down the line, but will also help you stay organized as you write your code.\n",
    "\n",
    ">*Our code was written to look for our dataset in the same directory as this JN, in a folder called \"data\". However, if your CSVs are in a different directory, you can simply input your correct directory address in place of data/gapminder_gdp_oceania.csv -- just be sure to include quotation marks around your directory, for example:*\n",
    ">\n",
    ">*`pd.read_csv('c:\\users\\yourname\\downloads\\data\\gapminder_gdp_oceania.csv', index_col='country')`*\n",
    "\n",
    "You'll notice we are also assigning the column named 'country' as our index. An index refers to a position within an ordered list. In our case, the name of the country makes the most sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \\\n",
      "country                                                                       \n",
      "Australia       10039.59564     10949.64959     12217.22686     14526.12465   \n",
      "New Zealand     10556.57566     12247.39532     13175.67800     14463.91893   \n",
      "\n",
      "             gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \\\n",
      "country                                                                       \n",
      "Australia       16788.62948     18334.19751     19477.00928     21888.88903   \n",
      "New Zealand     16046.03728     16233.71770     17632.41040     19007.19129   \n",
      "\n",
      "             gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007  \n",
      "country                                                                      \n",
      "Australia       23424.76683     26997.93657     30687.75473     34435.36744  \n",
      "New Zealand     18363.32494     21050.41377     23189.80135     25185.00911  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# switching to reading in tab delimited file (the format dataverse converts csv to)\n",
    "#data = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')\n",
    "data = pd.read_csv('data/gapminder_gdp_oceania.tab', index_col='country', sep='\\t')\n",
    "\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With <code>print(data)</code> we can see that our CSV has been properly loaded and assigned to the \"data\" variable. Next we'll want to clean and standardize the data to make it easier for us to work with. The first thing we'll do is simplify column names. In addition to the 'country' column we have columns that refer to different dates. Fortunately these date columns all follow the same naming convention, which will make it that much easier to clean the data. Common to all date columns is that the column name always starts with \"gdpPercap_XXXX\" where \"XXXX\" refers to the four digit year. For clarity, we're going to remove the first portion of each column name using the .strip() function, leaving behind only the year.\n",
    "\n",
    "<blockquote><i>If you're curious, once you have your csv loaded into your 'data' variable, you can check to see what the columns are named by using the following code:<br><br>\n",
    "<code>print(data.columns)</code><br><br> You should see a list of strings like this:<br><br> <code>Index(['gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962', 'gdpPercap_1967',\n",
    "       'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982', 'gdpPercap_1987',\n",
    "       'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002', 'gdpPercap_2007'],\n",
    "      dtype='object')</blockquote></i></code>\n",
    "\n",
    "\n",
    "Those names are unnecessarily long, and we are only interested in the dates. In programming parlance, we are going to remove each instance of a string, or set of characters. In our case, this means going through each column in the dataset and removing all instances of our string. Surprisingly, this is very simple to do and can be done with a single line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = data.columns.str.strip('gdpPercap_')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<blockquote><i>The syntax here is relatively simple. We are telling the program to create the variable years, to which we are going to access our other variable, data. However, from that variable we want only the column headers, which we communicate with the <code>.columns</code> addition. Furthermore, because the <code>.strip()</code> command only works on strings, we need to make sure that everything that is being parsed during our <code>.strip()</code> command is a string. We do that by simply adding a <code>.str</code> to our line of code. In plain English, <code>data.columns.str.strip('gdpPercap_')</code> says from the variable data, access each column, turn the value into a string, and then strip all strings that are an exact match of <code>gdpPercap_</code></blockquote></i>\n",
    "\n",
    "Now we have a variable named years that contains a list of the corrected column names. We can check with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['1952', '1957', '1962', '1967', '1972', '1977', '1982', '1987', '1992',\n",
      "       '1997', '2002', '2007'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to rename each column with the corresponding date in our list. During this step we also need to convert these numbers from strings into integers. Converting to integers is necessary for our matplotlib module to work.\n",
    "\n",
    "The following code is reassigning values to the columns in our dataset while simultaneously converting these values into integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = years.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data.columns will iterate over each column name just like before, but this time instead of reading these values we are going to assign new values to each. Specifically we are iterating through our list, years, and assigning each of those values to our columns as integers. Now if we were to print the columns, instead of the clunky list we saw before we should see a list of dates, exactly like we saw in our years variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002,\n",
      "            2007],\n",
      "           dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our data's been sufficiently cleaned we can start plotting data directly from our variable, data, which contains what is known as a Pandas dataframe. Let's plot a quick line graph:\n",
    "\n",
    "<blockquote><i>For more information on Pandas dataframes, follow this link:</i><br> https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html </blockquote>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc610b02e10>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc['Australia'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At a glance, the graph suggests that our data is cleaned sufficiently for us to start graphing. We can take a look ourselves by looking at the head or the tail of the dataset; that is to say, the first five rows or the last five rows, respectively. To do that, we use the <code>.head()</code> or <code>.tail()</code> function, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first five rows of our data are:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1952</th>\n",
       "      <th>1957</th>\n",
       "      <th>1962</th>\n",
       "      <th>1967</th>\n",
       "      <th>1972</th>\n",
       "      <th>1977</th>\n",
       "      <th>1982</th>\n",
       "      <th>1987</th>\n",
       "      <th>1992</th>\n",
       "      <th>1997</th>\n",
       "      <th>2002</th>\n",
       "      <th>2007</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Australia</td>\n",
       "      <td>10039.59564</td>\n",
       "      <td>10949.64959</td>\n",
       "      <td>12217.22686</td>\n",
       "      <td>14526.12465</td>\n",
       "      <td>16788.62948</td>\n",
       "      <td>18334.19751</td>\n",
       "      <td>19477.00928</td>\n",
       "      <td>21888.88903</td>\n",
       "      <td>23424.76683</td>\n",
       "      <td>26997.93657</td>\n",
       "      <td>30687.75473</td>\n",
       "      <td>34435.36744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New Zealand</td>\n",
       "      <td>10556.57566</td>\n",
       "      <td>12247.39532</td>\n",
       "      <td>13175.67800</td>\n",
       "      <td>14463.91893</td>\n",
       "      <td>16046.03728</td>\n",
       "      <td>16233.71770</td>\n",
       "      <td>17632.41040</td>\n",
       "      <td>19007.19129</td>\n",
       "      <td>18363.32494</td>\n",
       "      <td>21050.41377</td>\n",
       "      <td>23189.80135</td>\n",
       "      <td>25185.00911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    1952         1957         1962         1967         1972  \\\n",
       "country                                                                        \n",
       "Australia    10039.59564  10949.64959  12217.22686  14526.12465  16788.62948   \n",
       "New Zealand  10556.57566  12247.39532  13175.67800  14463.91893  16046.03728   \n",
       "\n",
       "                    1977         1982         1987         1992         1997  \\\n",
       "country                                                                        \n",
       "Australia    18334.19751  19477.00928  21888.88903  23424.76683  26997.93657   \n",
       "New Zealand  16233.71770  17632.41040  19007.19129  18363.32494  21050.41377   \n",
       "\n",
       "                    2002         2007  \n",
       "country                                \n",
       "Australia    30687.75473  34435.36744  \n",
       "New Zealand  23189.80135  25185.00911  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The first five rows of our data are:\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that in our case, we only have two rows in total, so if you were to run the .tail() function instead, you would get the same result. But what if you wanted to reverse the table by transposing the rows to the columns and vice versa? In other words, instead of having `gdpPercap_<year>` in the colums across the top, we have Australia, New Zealand and the gdpPercap years as columns?   We can simply use the `T` function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>country</th>\n",
       "      <th>Australia</th>\n",
       "      <th>New Zealand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1952</td>\n",
       "      <td>10039.59564</td>\n",
       "      <td>10556.57566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1957</td>\n",
       "      <td>10949.64959</td>\n",
       "      <td>12247.39532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1962</td>\n",
       "      <td>12217.22686</td>\n",
       "      <td>13175.67800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1967</td>\n",
       "      <td>14526.12465</td>\n",
       "      <td>14463.91893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1972</td>\n",
       "      <td>16788.62948</td>\n",
       "      <td>16046.03728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1977</td>\n",
       "      <td>18334.19751</td>\n",
       "      <td>16233.71770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1982</td>\n",
       "      <td>19477.00928</td>\n",
       "      <td>17632.41040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1987</td>\n",
       "      <td>21888.88903</td>\n",
       "      <td>19007.19129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1992</td>\n",
       "      <td>23424.76683</td>\n",
       "      <td>18363.32494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997</td>\n",
       "      <td>26997.93657</td>\n",
       "      <td>21050.41377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>30687.75473</td>\n",
       "      <td>23189.80135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007</td>\n",
       "      <td>34435.36744</td>\n",
       "      <td>25185.00911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country    Australia  New Zealand\n",
       "1952     10039.59564  10556.57566\n",
       "1957     10949.64959  12247.39532\n",
       "1962     12217.22686  13175.67800\n",
       "1967     14526.12465  14463.91893\n",
       "1972     16788.62948  16046.03728\n",
       "1977     18334.19751  16233.71770\n",
       "1982     19477.00928  17632.41040\n",
       "1987     21888.88903  19007.19129\n",
       "1992     23424.76683  18363.32494\n",
       "1997     26997.93657  21050.41377\n",
       "2002     30687.75473  23189.80135\n",
       "2007     34435.36744  25185.00911"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GDP per capita')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.T.plot()\n",
    "plt.ylabel('GDP per capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many preconfigured styles for graphs. Next we'll use one called \"ggplot\" to make a bar graph. Note the x-axis ticks are now vertical, allowing for more dates to fit into the same amount of space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GDP per capita')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "data.T.plot(kind='bar')\n",
    "plt.ylabel('GDP per capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we've only been using Python's most basic graphing functions. For more detailed or otherwise more sophisticated graphs, we'll need to use the plot function from the matplotlib module.\n",
    "\n",
    "Up until now we've relied on Python's default plotting behavior to produce our graphs. Modules like matplotlib allow us to exercise more granular control over our data. As an example, next we'll make two variables that we will use to fill in our x- and y- axis data: years will be our x-axis values and gdp_australia will be our y-axis values. The three comma-separated values that are contained within the .plot parentheses are not only how we control which of our data we want to feed into our graph, but also allow us to customize the visualizations. In the following example, our .plot() function has input values for our graph's x-axis, y-axis, and the style of our line (in this case, a green dashed line represented by <code>g--</code>):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc660949d90>]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = data.columns\n",
    "gdp_australia = data.loc['Australia']\n",
    "\n",
    "plt.plot(years, gdp_australia, 'g--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib enables us to plot multiple sets of data together on the same graph. In addition to assigning our x-axis values to a variable, this time we'll also assign values to our y-axis. Let's also add some labels to each axis, as well as a legend to label our lines. Note that it appears as though we only gave instructions on the location of the legend. In reality, matplotlib is able to populate the legend because we gave each of our line plots a label in our <code>.plot()</code> function. Moreover, specifying the location of the legend is optional; matplotlib will try and place the legend in a suitable place on its own by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GDP per capita ($)')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEJCAYAAACzPdE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeVxU1fvA8c8MM6zDMgwuieIGbqiB4W4qQu7b1yUX0tRcyrLMXFJTM9Mo10zNNVIxxd0sVzSlMhNzBxVNzRXBGQSGfZj7+4OfkxQoGjAwnPfr1auZO3fmPodRHs895zxHJkmShCAIgiD8R3JzByAIgiBYBpFQBEEQhEIhEoogCIJQKERCEQRBEAqFSCiCIAhCoRAJRRAEQSgUCnMHYG537941PXZzc+PBgwdmjKboWHLbwLLbJ9pWellq+ypVqpTncdFDEQRBEApFsfRQMjMzmTFjBgaDgezsbJo1a8arr77K0qVLiY6Oxt7eHoC3336batWqIUkSISEhnD59GhsbG0aPHk2NGjUAOHLkCNu3bwegV69etG3bFoBr166xdOlSMjMz8fX1ZejQochksuJoniAIgkAxJRSlUsmMGTOwtbXFYDAwffp0fHx8ABg0aBDNmjXLdf7p06eJjY1l8eLFXLlyhdWrVzNnzhz0ej1bt24lODgYgA8//BA/Pz9UKhWrVq1i1KhReHl58dlnn3HmzBl8fX2Lo3mCIAgCxZRQZDIZtra2AGRnZ5Odnf3E3sPJkydp3bo1MpmMWrVqkZKSQkJCAlFRUTRs2BCVSgVAw4YNOXPmDN7e3qSlpVGrVi0AWrduTWRk5HMlFEmSSE9Px2g0WlQP5/79+2RkZJg7jGciSRJyuRxbW1uL+i4EwVIV26C80Whk0qRJxMbG0qFDB7y8vDhw4AAbN25k69at1K9fn6CgIJRKJTqdDjc3N9N7NRoNOp0OnU6HRqMxHXd1dc3z+KPz8xIeHk54eDgAwcHBua6jUChMv8CUSmVh/wjMzsbGxtwhPLOsrCzkcnmu7zcvCoUi13dpSUTbSi9Lb98/FVtCkcvlzJ07l5SUFObNm8fNmzcZOHAgLi4uGAwGVqxYwa5du+jTpw951avM71+oMpksz/PzExgYSGBgoOn54zMw3Nzc0Ov1ODg4YDAYnqF1JZ9CoSiVbZLJZOj1+qd+x5Y6mwZE20ozS21fiZnl5eDgQL169Thz5gxqtRqZTIZSqcTf35+rV68COT2Mx78ErVaLWq3G1dUVrVZrOq7T6VCr1Wg0mlzHtVotrq6uzxWfuLVS8ojvRBBKh2JJKElJSaSkpAA5M77Onz+Pu7s7CQkJQM698sjISKpUqQKAn58fERERSJJETEwM9vb2qNVqfHx8OHv2LHq9Hr1ez9mzZ/Hx8UGtVmNnZ0dMTAySJBEREYGfn19xNE0QBKFUuXbNiuBgR4zGwv/sYrnllZCQwNKlSzEajUiSRPPmzXnppZeYOXMmSUlJAFStWpWRI0cC4Ovry6lTp3j33XextrZm9OjRAKhUKnr37s3kyZMB6NOnj2mAfvjw4SxbtozMzEx8fHxK/QyvvXv3Mnz4cI4ePYqnp+czv3/fvn3UqFHDNFGhoMLCwjh37hyzZ89m3bp12NnZ0bdv32e+viAIJU9srJyBAzWkpMgYPDiFSpUKN6vIyvoGW/9cKX/z5k3TuhhzGjVqFHFxcbRq1YoPPvjgmd8/duxYAgMD6dq1K5B7DMVgMKBQ5P1viccTSkmRmpr61O/EUu9Vg2hbaVaS2peYKKN3bzdu3rRiyxYtL76Y9dyfVWLGUISnS0lJ4eTJk8ybN49du3YBcOzYMQYPHmw6Z+rUqYSFhQEwZ84c2rZtS2BgIJ988gmRkZEcPHiQTz/9lFdeeYUbN27wv//9j88++4zevXuzevVqDhw4QNeuXWnfvj39+vUjPj7+X3HMnz+f5cuXA7BhwwY6d+5MYGAgI0aMIC0trRh+EoIgFIa0NBgyxJWrVxWsXq37T8nkScp8La8nmT7diejowp0+XK9eFp98kvTEc/bt20fbtm2pWbMmLi4unD9/Pt9zExIS2Lt3LxEREchkMhITE3F2duaVV17J1UOBnLGsbdu2AfDw4UN2796NTCbju+++Y9myZcyYMSPf63Tq1ImgoCAAPv/8czZu3MiwYcOepemCIJiBwQCjR6uJjLRm2bIEWrfOLLJriYRSAu3cuZMRI0YA0KNHD3bu3ElAQECe5zo6OmJjY8P48eMJCAjINSX6n7p37256fO/ePd566y3i4uLIzMzEw8PjiTFdvnyZL774wjTBok2bNs/RMkEQipMkwaRJzhw4YMfs2Q/p3j29SK8nEsoTPK0nURR0Oh3Hjh3j8uXLyGQyU1WB9u3b51qL8WjVu0Kh4Mcff+SXX35h165dhISEsGXLljw/+/FxiGnTpjFy5Ejat2/PsWPHWLBgwRPjev/991mzZg3e3t6EhYXx22+/FUJrBUEoSsHBjmza5MD77yczZEhqkV9PjKGUMD/++CO9e/fmxIkT/P7775w8edLUe4iJiSEjI4OkpCR++eUXIGe8JTk5mYCAAGbOnEl0dDSQMyPu0VTtvCQlJVGxYkWAfBPQ4/R6PRUqVCArK4sdO3b812YKglDEVq1yYMkSR157LYUPPkgulmuKHkoJs2vXLt5+++1cxzp37syOHTvo1q0bgYGBVK9enfr16wM5v+iHDRtGRkYGkiSZxkF69OjBhAkTWLNmDStXrvzXdT744ANGjRpFxYoVadSoEbdu3XpiXBMmTKBr165UrlyZOnXqoNfrC6nFgiAUtu3b7fj4Y2c6d05jzpxEimttsJg2XEKnDRe20lp6BcS0YdG20ssc7fvpJxuGDHGlceNMQkO1/H9d3kIlpg0LgiBYuFOnlIwYoaZOnSxCQnRFkkyeRCQUQRAEC3DlioJBgzRUqGAkNFSHo2Px33wSCUUQBKGUu3NHzsCBriiVEt99p6VcuSIo1FUAYlBeEAShFNPpZAQFaUhOlrN16wOqVs02WywioQiCIJRSqakyXn9dw82bCkJDtdSvb96JNyKhCIIglEJZWTBqlJozZ5SsXJlAixZFV1KloMQYSgnk7u7OzJkzTc+XL1/O/PnzC+3zv/32W1555RXTf+3atcPd3Z0rV6481+d5eXkVSly3bt2iXbt2hfJZgmDJjEYYN86Fw4dtCQ5OpFOnoi2pUlAioZRANjY27N27F51OVySfP2TIEA4ePGj675VXXqFXr16FlhgEQSg6kgSzZjmxfbs9EyYkERRU9CVVCkoklBLIysqKoKCgPFe4a7VaRowYQefOnencuTORkZEABAQEkJiYiCRJeHt7m8qpjBkzhoiIiHyvdfz4cX744QfmzJkDQHZ2NrNmzTKVql+/fj2QU+Ll1VdfpUOHDgQEBLB///5/fVZ+59y6dYs2bdowYcIE/P39GTBggKn8/blz5wgMDKRbt258++23z/9DE4Qy4uuvVaxcqWLYMD3vvVeyKlaIMZQnmP7bdKK10YX6mfU09fik+SdPPW/IkCEEBgaadqs0xTR9OiNGjKBJkybcuXOHgQMHcvToUfz8/IiMjKRy5cpUrVqVEydO0LdvX06dOkVwcHCe10hMTGTcuHF8+eWXODo6ArBx40YcHR3Zs2cPGRkZ9OzZkzZt2lCpUiXWrFmDo6MjOp2Obt260b59+1z7vdvY2OR5DsD169dZunQpc+fOZdSoUezZs4fevXszbtw4Zs2aRfPmzZk1a9bz/lgFoUwIC7Nj9mwnevRIZebMpGIrqVJQIqGUUI6OjvTp04c1a9ZgZ2dnOv7zzz8TExNjeq7X69Hr9TRt2pTff/+d27dvM3jwYEJDQ7l37x5qtRoHB4c8rzF58mR69epF48aNTceOHj3KxYsX+fHHHwFITk7m+vXrvPDCCwQHB/P7778jk8mIjY0lPj6e8uXLm94rSVKe5wBUqVLFVH+sYcOG3Lp1i6SkJBITE2nevDkAvXv35qeffiqkn6AgWJYDB2yYMMGF1q3TWbToIfISeH9JJJQnKEhPoigNHz6cjh070q9fP9Mxo9HI999/nyvJADRt2pRvv/2WypUrM2nSJPbu3cuPP/5IkyZN8vzszZs3c+vWLRYvXvyv1z799FPatm2b61hYWBharZa9e/eiVCpp2rSpqYT+I9u3b8/3HBsbG9N5VlZWpKenI0lSrh6OIAh5O3HCmrfecqVBgyxWrUrA2trcEeWtBOY44RG1Wk23bt3YuHGj6VibNm1yjTVcuHAByJkZptPpuH79OlWrVqVJkyYsX76cpk2b/utz//rrLz7//HOWLFnyr73l27Rpw7p168jKytki9M8//yQ1NZXk5GTc3NxQKpX8+uuv3L59+1+fW5BzHufs7IyTkxMnTpwAEGXxBSEPFy8qGDLElUqVslm3TodKVXLr+YqEUsKNGjUq12yvWbNmcfbsWQIDA2nbtq1p0BzA19eXGjVqANCkSRNiY2Nz3c56ZOnSpaSlpTF8+PBc04d///13Bg4ciJeXFx07dqRdu3ZMmjQJg8FAr169OHv2LJ06dWLHjh14enr+63MLcs4/LViwgClTptCtWzdsi7uSnSCUcLduWfHaaxrs7CQ2btSi0ZinpEpBifL1onx9iSfK14u2lVb/pX1arZyePd3QauVs3/6AOnVKzt/f/MrXizEUQRCEEkavlzFokCt371qxaZO2RCWTJxEJRRAEoQTJyIDhw125cEHJmjU6Gjc2f0mVghIJ5R/K+B3AEkl8J0JZYTTC2LFqfv7ZhgULEnjllYynv6kEEYPy/yCXy0vtWIMlMhgMyEvihHtBKGSSBNOnO/H993ZMnZpEv35p5g7pmYkeyj/Y2tqSnp5ORkaGRa2RsLGx+de6kZJOkiTkcrmY/SWUCV9+qSIkRMWoUXreeqtklVQpKJFQ/kEmk/1r0aAlsPTZNIJQmq1fb8/cuU707p3KRx+VvJIqBSXuJQiCIJjRjz/aMmWKM+3apTN/fsksqVJQpTh0QRCE0i0iwpp33lHTqFEWK1YkoFSaO6L/RiQUQRAEMzh1Sskbb7hSs6aBtWu12NuX/tmMxTKGkpmZyYwZMzAYDGRnZ9OsWTNeffVV4uLiWLRoEXq9nurVqzNmzBgUCgVZWVksWbKEa9eu4ejoyNixY01VbXfs2MHhw4eRy+UMHToUHx8fAM6cOUNISAhGo5GAgAB69uxZHE0TBEF4ZpcvKxg0SEO5ckY2bNDi4lL6kwkUUw9FqVQyY8YM5s6dyxdffMGZM2eIiYkhNDSULl26sHjxYhwcHDh8+DAAhw8fxsHBga+++oouXbqwYcMGAG7fvs2xY8dYsGABU6dOZc2aNRiNRoxGI2vWrGHKlCksXLiwQIUJBUEQzOHWLSsGDtRgbZ1Tn6tChZJdn+tZFEtCkclkpqmf2dnZZGdnI5PJiIqKolmzZgC0bdvWtPvgyZMnTeXTmzVrxoULF5AkicjISFq0aIFSqaR8+fJUrFiRq1evcvXqVSpWrEiFChVQKBS0aNHC9FmCIAglRXy8nP79NaSlyfjuOy1Vq2abO6RCVWzTho1GI5MmTSI2NpYOHTpQoUIF7O3tsbKyAsDV1dVUVVen06HRaICcvTPs7e1JTk5Gp9Pl2vf88fc8Ov/R4ytXruQZR3h4OOHh4QAEBwfj5uZmek2hUOR6bkksuW1g2e0TbSu9Hm9fYiK8/rqC+/dl7N1roHlzFzNHV/iKLaHI5XLmzp1LSkoK8+bN486dO/mem1epDZlMlm8JjvzOz0tgYCCBgYGm54+vzbDktRqW3Daw7PaJtpVej9qXliYjKMiV6GgZISE6vLwyKM3Nzq/acLHP8nJwcKBevXpcuXKF1NRUsrNzunw6nQ5XV1cgp4eh1WqBnFtkqampqFSqXMcff88/j2u1WtRqdTG2ShAEIW9ZWTBqlJoTJ6z58ssE/P1LV8WKZ1EsCSUpKYmUlBQgZ8bX+fPncXd3x9vbm+PHjwNw5MgR/Pz8AHjppZc4cuQIAMePH8fb2xuZTIafnx/Hjh0jKyuLuLg47t27h6enJzVr1uTevXvExcVhMBg4duyY6bMEQRDMxWiEceNcOHTIljlzEunRI93cIRWpYrnllZCQwNKlSzEajUiSRPPmzXnppZeoXLkyixYtYtOmTVSvXp127doB0K5dO5YsWcKYMWNQqVSMHTsWgCpVqtC8eXPGjRuHXC7njTfeMBUOHDZsGLNnz8ZoNOLv70+VKlWKo2mCIAh5kiQYP96K7dutmTgxicGDU80dUpETOzb+Y8dGS72fa8ltA8tun2hb6bRwoYp585wYMULPjBmltz5XXkrMGIogCIKlCwmxZ948JwYNymb6dMtKJk8iEoogCEIh2rHDjo8+cqFDhzSWL88u1cUen1UZaqogCELROnTIhrFjXWjePINlyxJQlLENQkRCEQRBKAQnTlgzcqQr9eplERKioyzuC1eg/PngwQP++usvUlJScHBwoGrVqha9ulUQBOFZREUpeP11V9zdDYSG6nB0LJtznfJNKAaDgfDwcA4ePEhcXBwVK1Y0bY8bGxtL+fLleeWVVwgMDERR1vp1giAI/+/6dSuCgjQ4OEhs3KhDo7GcYo/PKt9MMGHCBOrXr8/IkSPx8vIyrfeAnLpcV69e5eeff2bixIksWLCgWIIVBEEoSWJj5QwYoCE7G7Zu1eLublnFHp9Vvgnl448/xtnZOc/X5HI5tWrVolatWiQlJRVZcIIgCCVVQoKMgQM16HRytmzR4ulpMHdIZpdvQskvmfyTk5NToQUjCIJQGqSmyhg8WMP16wpCQ7W8+GKWuUMqEZ46+BEfH4+NjY0pcZw8eZJDhw7h7OxM//79cXGxvBLMgiAI+cnIgOHD1Zw5o2TVqgRatsw0d0glxlOnDS9YsIC4uDggJ7l89dVX1K5dG4PBwNKlS4s8QEEQhJIiOxvefVfN0aO2zJv3kI4dLbvY47PKt4cSHR0NwL1790hLSyM6Opo//viDGjVqUKtWLTw9PVm4cKHpvHr16hVPxIIgCGYgSTB5sjM//GDHtGmJ9OuXZu6QSpx8E8qjXokkScTHxyOXy4mKisLT05P4+HgkSUImk5kei4QiCIIlCw52ZMMGB955J5k330wxdzglUr4J5dGe7hEREdy8eRNPT0/i4uKYOHEirq6upKens23bNtq0aVNcsQqCIJjF8uUOLFniyGuvpfDhh8nmDqfEeuoYyvDhw7lz5w67d+9myJAhpl0Vf//9d1q0aFHkAQqCIJhTWJgds2Y5061bGnPmJJaZysHP46mzvCpVqsTUqVP/dVz0TARBsHR799oyfrwLbdqks3hxAlZW5o6oZMu3h2IwFGyRTlaWmH8tCILl+fVXa0aPVuPjk8Xq1QlYW5s7opIv34Qyfvx4du3ahU6ny/P1hIQEdu3axcSJE4ssOEEQBHM4e1bJ0KGu1KhhYN06Lfb2ZbPY47PK95bXJ598ws6dO5kwYQIqlYoXXngBOzs70tLSuHfvHqmpqbRp04aZM2cWZ7yCIAhF6tIlBUFBrmg0RjZs0KJWi2RSUPkmFCcnJwYPHszAgQO5cuUKN2/eJCUlBZVKhYeHB56enqLKsCAIFuXaNSv699dgYwMbN2qpWLHsVg5+Hk/NCAqFgrp161K3bt3iiEcQBMEsbt+2ol8/DUYjbNmipVq1sl05+HmILoYgCGVebKycfv00pKTI2bLlAV5eonLw8xAJRRCEMk2rldO/v4b4eDmbNmnx9hbJ5HmJhCIIQpmVmChjwAANt25ZERqqo1EjsQzivxAJRRCEMkmvl/HaaxquXFEQEqKjeXNRhv6/KnBCuXHjBhcvXiQ5ORlJ+nsaXb9+/YokMEEQhKKSlgZDhrhy9qySlSsTaNs2w9whWYQCJZTw8HDWrl1Lw4YNOXPmDD4+Ppw7dw4/P7+ijk8QBKFQZWTAiBGuHD9uzZIlYk+TwvTU4pAAu3btYsqUKUyYMAFra2smTJjAuHHjsBKFbQRBKEUMBnjnHTU//WTL3LmJ9Owp9jQpTAVKKElJSaZ1KDKZDKPRiK+vL3/88UeRBicIglBYsrPh/fdd2LPHjk8+SWTAgFRzh2RxCnTLy9XVlbi4OMqXL88LL7zAyZMncXR0FCvlBUEoFR7ttrh9uz0ffpjEG2+IDbKKQoEyQo8ePbhz5w7ly5enT58+LFiwAIPBwJAhQ4o4PEEQhP9GkuDjj53YsMGBd99NZswYvblDslgFSiiPdm8E8PX1JSQkBIPBgK2tbVHFJQiCUCi++MKR1atVvPGGnokTxW6LRalACWXixIl88cUXf79JoUChUPDhhx8SHBz81Pc/ePCApUuX8vDhQ2QyGYGBgXTu3JnNmzdz6NAhnJycABgwYACNGjUCYMeOHRw+fBi5XM7QoUPx8fEB4MyZM4SEhGA0GgkICKBnz54AxMXFsWjRIvR6PdWrV2fMmDHilpwglHFffaVi8WJHgoJSmDkzSey2CJy8f5Jdf+7ik+afICvkH0iBfuPGxsb+65gkSdy/f79AF7GysmLQoEHUqFGDtLQ0PvzwQxo2bAhAly5d6N69e67zb9++zbFjx1iwYAEJCQnMmjWLL7/8EoA1a9bw0UcfodFomDx5Mn5+flSuXJnQ0FC6dOlCy5YtWblyJYcPH6Z9+/YFik8QBMuzZo0DwcFO9OqVymefia17Y1NimXNiDtuubqOifUXeavgWlVSVCvUaT0woS5YsAXJ2b3z0+JH4+HiqVKlSoIuo1WrUajUAdnZ2uLu757txF0BkZCQtWrRAqVRSvnx5KlasyNWrVwGoWLEiFSpUAKBFixZERkbi7u5OVFQU7733HpBzi27Lli0ioQhCGbVxoz3TpzvTqVMaCxc+LNNb92ZkZ7D6/GoWnV6EwWhgjM8YxviMwUHpUOjXemJCefSL+5+PZTIZtWvXpnnz5s98wbi4OK5fv46npyeXLl1i//79REREUKNGDQYPHoxKpUKn0+Hl5WV6j6urqykBaTQa03GNRsOVK1dITk7G3t7etC7m8fP/KTw8nPDwcACCg4Nxc3MzvaZQKHI9tySW3Daw7PaJtj2bsDA5EyZY0b69kbAwK2xszPezM+d3J0kSe67uYcKhCfyZ8CfdvLrxecDn1FTXLLJrPjGh9O3bFwAvLy/TGMZ/kZ6ezvz58xkyZAj29va0b9+ePn36ABAWFsa6desYPXp0rtIuj8vr+LPeAwwMDCQwMND0/MGDB6bHbm5uuZ5bEktuG1h2+0TbCm7fPltGjlTTrFkmy5ZpSU6GZDOOw5vru7v68Cozj8/k8K3DeLp48l2n72hTuQ1kUyjxVKqU962yfBNKdHQ09erVyzlJoeDChQt5nle/fv0CBWAwGJg/fz4vv/wyTZs2BcDFxcX0ekBAAJ9//jmQ0/PQarWm13Q6Ha6urgC5jmu1WtRqNY6OjqSmppKdnY2VlVWu8wVBKBuOHLHhrbfUvPhiFt9+q8POztwRFb/kzGQWnV7E6vOrsVPY8XGzjxniPQSlXFks1883oaxZs4b58+cD8PXXX+d5jkwm+9fYSl4kSWL58uW4u7vTtWtX0/GEhATT2MqJEydMYzJ+fn4sXryYrl27kpCQwL179/D09ESSJO7du0dcXByurq4cO3aMd999F5lMhre3N8ePH6dly5YcOXJE1BkThDLkt9+seeMNV7y8DKxfr0WlKlv7wBslI1uubOGzE5/xIO0B/Wv358PGH+JmV7y322RSfveXCtGlS5eYPn06Hh4epltUAwYM4Ndff+XGjRvIZDLKlSvHyJEjTQlm+/bt/PTTT8jlcoYMGYKvry8Ap06dYu3atRiNRvz9/enVqxcA9+/f/9e0YaXy6Vn57t27psfi1kLpZcntE217slOnlPTvr6FSpWy2bdOi0ZScfeCL47s7FXeK6cemczr+NC+Vf4lZLWbxYrkXi/Sa+d3yKnBCMRqNxMTEkJCQgKurK15eXsjlBSoFVqKJhGIZLLl9om35u3BBwauvuqFWG9m27QEVK5acZAJF+93FpcYx58QctlzZQgX7CkxpMoVenr2Qy4r+9/Izj6E87q+//mLu3LlkZWWZZlAplUrGjx9PtWrVCjNOQRCEArlyRcHAgRocHIyEhWlLXDIpKpnZmXwT9Q0LTy0kIzuDt198m3d93kVlrTJ3aAVLKF9//TUdOnSga9euyGQyJEnixx9/5OuvvzYNpAuCIBSXGzes6N9fg1wOmzZpqVw529whFYvDtw4z47cZXEu8RqBHIDOazaCGcw1zh2VSoL7RvXv36NKli2n8QyaT0blz5zxX0AuCIBSlO3fk9OunISMjJ5nUrGn5yeRa4jUG7xvMoH2DAFjfcT1rO6wtUckECthD8fX15eTJkzRp0sR07OTJk6aBckEQhOIQFyenXz83EhPlbN6spU4dg7lDKlL6TD2Lzyxm5fmV2FjZMK3pNIZ5D8PaytrcoeWpQAnFaDSyaNEiatSoYVojcu3aNfz8/HJNG37nnXeKLFBBEMo2nU7GgAEaYmPlbNyoo2HDLHOHVGSMkpFtV7bxWeRn3E+9z6u1XmVy48mUty9v7tCeqEAJpUqVKrnqdlWuXJkXXyzaaWmCIAiP6HQygoI0XL+uYO1aLY0bZ5o7pCJzJv4M045N41TcKXzL+bL6ldU0Kt/I3GEVSIESyqMSLIIgCMXpr7+sWL3agU2b7MnMlLFmjY6XX7bMZBKfGk9wZDBhMWG42bmxoM0C+nr1LZZpwIWlwBuGGAwG7t69S1JSUq7jBS29IgiCUFB//KFkxQoVe/faIpdDjx5pvPWWnrp1LW/MJMuYRUhUCAv+WEB6djqjGo5irO9YHK0dzR3aMytQQrl06RILFiwgKyuLtLQ07OzsSE9PR6PRFKj0iiAIwtNkZ8P+/basWKHi5ElrnJ2NvPWWnqFDU3jhBctcY3I67jQTf55ItC6adlXa8XGzj6npUnTVgItagRLK2rVr6d69O127dmXo0KGEhISwdetWrK1L5kwDQRBKj9RUGWFhdqxereLGDQUeHgY++SSR/v1TcXCwzJpcyZnJfB75Od9Gf0sFh8kSUXEAACAASURBVAqsDlxNx2odC30HxeJWoIRy9+5dOnfunOtYz549efvtt/+126IgCEJB3L8v58svrVi5sgIPH8pp1CiTyZN1dOqUbrEbYkmSxJ4be5h+bDr3U+8zzHsYE/wmlMrbW3kpUEKxt7cnLS0NBwcHXFxcuH37NiqVivT09KKOTxAEC3PxooKVK1Xs2GGHwQCdOqUzcqSexo0tdxowwB39Hab+OpWDNw/irfFmTfs1+JT77/tMlSQFSihNmzbl9OnTtGrVinbt2jFz5kysrKyea8dGQRDKHkmCiAgbVqxw4OhRW+zsjLz2WgoTJljj7Jxg7vCKlMFo4Juob5h7ci4SEtOaTmN4/eEo5AWeE1VqFKhFQ4YMMT3u1q0bXl5epKWlibUogiA8UUYG7Nxpx6pVKi5eVFK+fDaTJiUxaFAKarX0/9V4zR1l0fnj3h+M3D2SC9oLBFQJYE7LOVR2rGzusIpMgRKKTqfD2toalSqnmmWdOnXQ6/U8fPhQ7IwoCMK/JCTICA11ICTEgfv3rahTJ4sFCxLo2TMNGxtzR1f09Jl6vvjjC0KiQihnV44VASvoUr1LqR90f5oCrZiZO3cuOp0u1zGdTse8efOKJChBEEqnGzes+OgjJxo3rkBwsBN16mSxYYOW8PB4+vUrG8lk/439tN3alm8ufMMI3xEc6XuErjW6WnwygWeY5eXh4ZHrmIeHB3fu3CmSoARBKF0iI5WsXJmzEFGhgJ490xg5Uk+9epa3EDE/d/R3mH5sOvv+2kdd17qsCFhBB+8OFrs5Wl4KlFCcnJyIjY2lYsWKpmOxsbE4OlrGVDdBEJ5ddjbs3ZuzEPHUKWtcXIy8/XbOQsSystkVQLYxm5DoEL44+QXZxmymNpnKiAYjUMqfvgW5pSlQQvH392f+/Pn079+fChUqEBsbS1hYGO3atSvq+ARBKIF++82a8eNduHFDQdWqBj799CGvvppmsQsR83P+wXkm/TyJsw/O4l/Znzkt5+Dh5PH0N1qoAiWUnj17olAoWL9+PVqtFjc3N/z9/enatWtRxycIQgmSmQnz5jmybJmKqlWzWbVKR4cOlrsQMT8pWSnM+2Meqy+sRmOrYVm7ZXSv0b1MjJM8SYESilwup3v37mJVvCCUYVevKnjnHRfOn7cmKCiFGTOSylyPBODAXweY+utU7qbc5bU6rzGlyRScbZzNHVaJYHkrawRBKFSSBGvX2jNrlhN2dhLffJPTKylr7qXcY/pv09lzfQ+11bXZGbCTxhUamzusEkUkFEEQ8hUfL+eDD1w4dMgWf/905s9/SIUKZWfAHXIG3dddXEdwZDAGo4EPG3/IqAajSuw2vOYkEoogCHk6eNCGDz5wQa+XM2tWIkOHplDWhgiitFFM+nkSp+NP09q9NZ+1+oxqTtXMHVaJ9dSFjUajkbCwMLKyLLtwmyAIOdLSZEye7MyQIRoqVDCyd288w4aVrWSSmpXKp79/SqcdnbiZfJMl/kv4rtN3Ipk8xVMTilwuZ//+/ViVtWkcglAGnTunpEMHN9atc+DNN/X88EM8tWuXncWJAJd0l2i/vT1fn/uafrX6cbTvUf7n+b8yP4OrIApUeqVNmzYcPHiwqGMRBMFMsrNhyRIV3bq5kZIiJyzsAdOmJZWJUimP2/XnLrru6kpKVgpbumxhbuu5qG3V5g6r1CjQGMrVq1fZt28f33//PRqNJlemnjlzZpEFJwhC0btzx4p333Xh+HEbunZNIzj4IWp12ZoObDAamH1iNivPr6RxhcasCFxBBfsK5g6r1ClQQgkICCAgIKCoYxEEoZjt2GHHlCnOGI2waFECffqklamxEoAHaQ9489Cb/HbvN4bWG8r0ZtPFDK7nVKCE0rZt2yIOQxCE4pSYKGPqVGd27LDHzy+Tr75KwMMj29xhFbs/7v/ByEMjeZj+kMVtF9Pbq7e5QyrVCpRQJEni0KFD/PrrryQnJzNv3jyio6N5+PAhLVq0KOoYBUEoRMePW/Puuy7ExloxfnwSY8boUZSxBQSSJBF6KZRpx6bxgsML7Oqxi/qa+uYOq9Qr0B+jsLAwzp8/T+fOnVm1ahUAGo2GtWvXFiihPHjwgKVLl/Lw4UNkMhmBgYF07twZvV7PwoULiY+Pp1y5crz//vuoVCokSSIkJITTp09jY2PD6NGjqVGjBgBHjhxh+/btAPTq1cvUe7p27RpLly4lMzMTX19fhg4dKmZlCMJjMjNh/nxHli7NqcO1c+cDGjUqe8sB0g3pTP11KptiNuFf2Z+v/L8SA++FpECzvI4ePcqkSZNo2bKl6Zd0+fLliYuLK9BFrKysGDRoEAsXLmT27Nns37+f27dvs3PnTho0aMDixYtp0KABO3fuBOD06dPExsayePFiRo4cyerVqwHQ6/Vs3bqVOXPmMGfOHLZu3Yperwdg1apVjBo1isWLFxMbG8uZM2ee+YchCJbq6lUFPXq4sWSJI/37p3LgQHyZTCa3k2/zv93/Y1PMJsb6jmVth7UimRSiAiUUo9GIra1trmPp6en/OpYftVpt6mHY2dnh7u6OTqcjMjKSNm3aADlTkyMjIwE4efIkrVu3RiaTUatWLVJSUkhISODMmTM0bNgQlUqFSqWiYcOGnDlzhoSEBNLS0qhVqxYymYzWrVubPksQyjJJgnXr7OnQwY1bt6xYvVrHvHmJZbKoY8TtCDru6Mj1xOuEtA9hgt8ErORifV1hKtAtL19fX9atW8frr78O5Nx/DAsL46WXXnrmC8bFxXH9+nU8PT1JTExErc7514FarSYpKQnI2V7Yzc3N9B6NRoNOp0On06HRaEzHXV1d8zz+6Py8hIeHEx4eDkBwcHCu6ygUilzPLYkltw0su33P27a4OBg1SsGePXICA42sWpVNpUoqQFX4QT6n4vjeJEli3vF5TD86nTqaOmzuvRkvV68iveYjlvznMi8FSiiDBw9myZIlDBkyBIPBwODBg2nYsCHvvPPOM10sPT2d+fPnM2TIEOzt7fM9T5L+/a+n/MZDZDJZnufnJzAwkMDAQNPzx7fndHNzs9jtOi25bWDZ7Xueth06ZMO4cS4kJ8v45JOcOlxyOZS0H1FRf2/Jmcm8f/R99t7YS/ca3ZnXeh4ORodi+7NiqX8uK1WqlOfxAiUUe3t7Jk6cSGJiIvHx8bi5ueHi4vJMARgMBubPn8/LL79M06ZNAXB2diYhIQG1Wk1CQgJOTk5ATg/j8S9Bq9WiVqtxdXUlOjradFyn01GvXj00Gg1arTbX+a6urs8UnyBYgrQ0GbNmObF2rQN162YRFqalTp2yVTrlkSsJV3jj4BvcSLrBjGYzGFF/hJioU8QKNIYCkJKSwrlz54iOjub8+fOmwfCCkCSJ5cuX4+7unmuXRz8/P44ePQrkDPw3btzYdDwiIgJJkoiJicHe3h61Wo2Pjw9nz55Fr9ej1+s5e/YsPj4+qNVq7OzsiImJQZIkIiIi8PPzK3B8glDaSRKcOGFNx45urF3rwMiROXW4ymoy+fH6j3TZ1YXEzEQ2dd7EyAYjRTIpBgXqoVy4cIF58+ZRqVIl3Nzc0Gq1rFmzhg8++IAGDRo89f2XL18mIiICDw8PJkyYAMCAAQPo2bMnCxcu5PDhw7i5uTFu3DggZ8zm1KlTvPvuu1hbWzN69GgAVCoVvXv3ZvLkyQD06dMHlSrnfvDw4cNZtmwZmZmZ+Pj44Ovr++w/DUEoRSQJzp9Xsnu3LT/8YMfNmwoqVsxm48YHtG6dae7wzMJgNPB55OcsO7cM3/K+rAxYSSVV3rdnhMInkwowAPH+++/Tt2/fXGtOfvvtN8LCwli0aFGRBljU7t69a3psqfc7wbLbBpbdvsfbJklw4YKSH36wZfduO/76S4FCIdGqVQbduqXRuXM6Tk6lZwZXYX5v2jQtow+P5pe7vzCo7iBmNp+JjZV5q1ta6p/L/zSGkpCQQLNmzXIda9KkCStWrPjvkQmC8EQ5SUTB7t12/PCDHTduKLCyykkiY8bo6dAhDVfX0pNEisLZ+LMMPzgcbbqWBa0X0K92P3OHVCYVKKG0bt2affv20blzZ9OxAwcO0Lp16yILTBDKMkmCqCgFP/xgx549Sv78szxWVhItW2bw9tt6OnZMx9XVfFvx3k6+zd4beylvXx5vjTfVnaqbbU3HxksbmXpsKm52buzstpOG5RqaJQ6hgAnl+vXrHDx4kO+//9609iMxMREvLy9mzJhhOk+UsheE5ydJEB2dk0R277bj+vWcnkibNhKjRiXSqZN5k4jBaODQzUOEXgrlp1s/IfF3r8hOYUdd17p4a7zx1nhTX1OfOq51sFPYFVk8GdkZTDs2jQ2XNvCy+8ssa7cMV1sxu9OcRPl6QTAjSYKLF/9OIteuKZDLJVq0yOTNN/V06pRO7dquPHiQarYY7+jvsPHyRjZe3khsSiwV7Svynu979K3Vl5SsFC5oLxCljSJaG82uP3ex/uJ6AOQyOTWda1JfU9+UaLw13mjsNE+5YsFiGhU+itPxp3nH5x0mvjRRrHovAUT5ekEoZpIEly49SiK2/PmnErlconnzTEaOzEkibm7m64kAZBuzOXzrMKGXQjl86zCSJNG2cltmt5hNoEcgCvnfvzq8Nd6mx5IkcSv5FlHaKKJ0UURpo/g99nd2/LnDdE5Fh4p4u+YklxbVW1DFugoejh7IZQVbxfDr3V9569BbpGensypwFZ2rd376m4RiUcaKVguC+Vy+nDOwvnu3LVev5iSRZs0yGT78IZ07mz+JANxLucemy5v47tJ33E25S3m78rz94tsE1QmiimOVp75fJpPh4eSBh5MHnap3Mh3XpeuI1kYTpY3igvYC0dpojtw+wuIziwFQKVW5ejHeGm9qqWvlmqUlSRIrzq9g9onZ1HCuwZpX1uDp4ln4PwThuYmEIghFKCbm7yRy5crfSWTYsJwkUq6c+ZNItjGbo3eOEnoxlPCb4WRL2bR2b83HzT+mfdX2KOXK/3wNV1tXWrm3opV7K9OxdEM6cVIcv/z5S06PRhtFWEwYKVkpAChkCrzUXqYE88f9P/jh+g90rt6Zha0XorIuOTXJhBwioQhCIcvOhv37bVm+XMUff1gjk+UkkSFDcpJI+fLmTyIAcalxpt7ILf0tNLYa3mz4JgPrDKSaU7Uiv76twpZGbo3wUHqYjhklIzeSbpgSTJQ2ip/v/MzWK1uRy+RMbTKVtxq+JVa9l1BPTSgpKSncv3+fF154ATu7opuxIQilXVqajLAwO1atUnHjhgIPDwMff5xIjx5pJSaJGCUjv9z5hfWX1nPgxgEMkoEWL7RgcpPJdKrWyex7qctlcmo416CGcw261ehmOh6fGk+mMRN3lbsZoxOe5okJ5dSpUyxcuJDMzExsbW2ZMGEC9euLbTIF4XHx8XJCQhxYu9aBhw/l+PpmMnmyjk6d0rEqIROPHqQ9IOxyGN9d/o4bSTdQ26h5o/4bBNUJoqZLTXOH91Tl7MuZOwShAJ6YUMLCwggKCsLf359Dhw6xadMmPv300+KKTRBKtCtXFKxc6cC2bfZkZkKHDum8+WYKfn6ZlIQ7MpIk8evdXwm9FMq+G/vIMmbRrGIzxr80nk7VOmGrKNgGeYJQUE9MKPfv36djx44AdOjQwbSXuyCUVZIEx49bs3y5ivBwW2xtJV59NZURI/TUrJlt7vCAnBlVm2M2E3oxlOtJ13GxceH1eq/zWp3X8FIXz8ZSQtn0xITyeN1IKysrsrNLxl8YQShuBgP8+KMtK1aoOHvWGlfXbD74IInXX09FozH/+IgkSfwe+zuhF0P58fqPZBozaVyhMWMbjaVL9S5FumJdEB55YkLJyMjIVVolPT0913MQ5VYEy6bXy9i40Z7Vqx24fVtB9eoGgoMf0qdPKiVhjkpSZhJbY7ay/uJ6Yh7G4GTtRFCdIF6r+xp1XOuYOzyhjHliQnnzzTdzPff39y/SYAShpLh3L2egff16B5KS5DRtmsGsWYkEBmYgL/C2dEXn/IPzrItex44/d5BmSONFtxeZ33o+PWr2EL0RwWyemFBEyRWhrLl4UcGKFSp27rQjOxs6d05n1Cg9jRplmTs00gxpfH/te9ZHr+d0/GlsrWzpWbMng+sN5sVyL5o7PEF4+jqUzMxMfvrpJy5evEhKSgoODg7Uq1ePtm3bYm1t3jnrglAYJAl+/tmG5csdOHrUFjs7I4MGpTB8eApVq5p/3PCy9jKLf1vMlpgtJGYm4uniyczmM+nj1QcXGxdzhycIJk9MKKmpqUybNg29Xk+DBg2oXr06Op2Obdu2sX//fmbNmoW9vX1xxSoIhSozE77/3o7ly1VcvKikXLlsJk1KYtCgFNRq825YlWXMYv+N/ay7uI5f7/6KQqagU/VODK47mOYvNBcrxYUS6YkJZefOnTg5OTF79mxsbf+es56ens7cuXPZuXMnAwcOLPIgBaEwJSXJ2LDBntWrVcTGWlGrVhYLFiTQs2caNubdMZY7+jt8d+k7Nl7eyP3U+7ir3PmkzSd0q9yN8vblzRucIDzFU1fKjx49OlcyAbC1tSUoKIilS5eKhCKUGklJMhYtcmTDBnv0ejktW2Ywd+5D2rY170C7UTJy9PZR1l1cR/jNcCRJwr+KP5+3+px2VdpRoXwFi9yXXLA8T0wo8fHxeHh45Pmah4cH8fHxRRKUIBS2Q4dsmDjRhbg4OT16pDFqVAoNGph3oF2bpiUsJozQi6H8lfwXGlsNoxuOJqhOEB5Oef+9E4SS7KmD8gpF3qcoFApxH1co8R4+lPHxx85s2WJPrVpZrF6tw9fXfIlEkiQi70ey/uJ6frj2A5nGTJpVbMakxpPoWK1jrv0/BKG0eWJCycrKIiwsLN/XDQZDoQckCIXlwAEbPvzQhQcP5IwZk8z77yebbYwkOTOZbVe3EXoxlIu6izgqHQmqE8SguoOo7VrbPEEJQiF7YkJp1aoVWq0239dbtmxZ6AEJwn+VkCBjxgxntm2zp06dLL79VkfDhsXfK5EkiWhdtGkBYkpWCvU19fni5S/oWbMnDkqHYo9JEIrSExPK6NGjiysOQSgU+/bZMnmyMzqdnLFjk3nvvWSedbmUUTKSmpVKclYy+kx9nv9PzkxGn6UnOTP343/+P1vKxtbKlu41uzO47mB8yvmIW8WCxXrqGIrBYDCNo1y6dAmj8e9CeLVr18aqpGz4IJRpDx7A22+7sHOnPfXqZbF+vZb69XPfko1NiWX71e3EpcblmwAe/V/i6etQ7BR2OCodUVmrTP+vals11/NKDpXoVqMbalt1UTVdEEqMJyaUAwcOcPnyZcaMGQPAp59+iqOjI5BTOPK1116jXbt2RR+lIDzBnj22TJ2qRKdTMn58Em+/rc/VK7n68Cpfn/2abVe3kWXMQqVU5fql76h0pIJDhVzPHa0dUSlV//q/k7UTKmsVKqUKhVzsoC0Ij3vi34ijR48yYsQI03OlUsnXX38NwI0bN1i1apVIKILZaLVypk51ZvduO3x8jISGPsDb++9eycn7J1l2dhn7/9qPrZUtQXWCGNlgJFWdqpoxakGwXE9MKHFxcVSrVs30vHLlyqbHVatWJS4ursgCE4Qn2b3blilTnElOljNxYhLTp9uSmGjAKBk5dPMQX5/7mt9jf8fFxoWxvmMZ5j0MjZ3G3GELgkV7YkJJT08nPT3dtFJ+1qxZptcyMjJIT08v2ugE4R/i4+VMmeLMnj12NGyYycKFWurUMSDJ5WyO2czyc8u5nHAZd5U7M5vPZEDtAWI2lSAUkycmFA8PD86dO0eTJk3+9dqZM2eoUqVKkQUmCI+TJNi1y46PPnIiJUXO5MlJvPmmnnSjnpXnv2NN1BpuJ9+mrmtdFrddTPea3VHKleYOWxDKlCcmlM6dO7N69WoA/Pz8kMvlGI1GTp48yTfffMPgwYOLJUihbIuLkzN5sjP79tnh65vJggVaXKvEMv/0GtZGryUxM5HWHq35rOVn+Ff2F9NyBcFMnphQWrZsiU6n46uvvsJgMODk5ERSUhJKpZI+ffrQqlWrAl1k2bJlnDp1CmdnZ+bPnw/A5s2bOXToEE5OTgAMGDCARo0aAbBjxw4OHz6MXC5n6NCh+Pj4ADm9opCQEIxGIwEBAfTs2RPIGetZtGgRer2e6tWrM2bMmHxLxgilhyTBjh12TJvmTFqajI8+SqR9vyhWRS9nyy9byMjOoFO1Trz14lu0r9deFFAUBDN76m/dbt26ERAQQExMDMnJyTg6OlKrVq1n2gelbdu2dOzYkaVLl+Y63qVLF7p3757r2O3btzl27BgLFiwgISGBWbNm8eWXXwKwZs0aPvroIzQaDZMnT8bPz4/KlSsTGhpKly5daNmyJStXruTw4cO0b9++wPEJJU9srJwPP3Th4EFbXnopk1Ezj/K9djFztu9BIVPQt1ZfRjYYiaeLp7lDFQTh/xXon/H29vamXsLzqFevXoFnhEVGRtKiRQuUSiXly5enYsWKXL16FYCKFStSoUIFAFq0aEFkZCTu7u5ERUXx3nvvATnJa8uWLSKhlFKSBFu22PHxx86kZ0DQ9B3ccF/AyJO/4Kh05K2Gb/FG/TeoYF/B3KEKgvAPZr0vtH//fiIiIqhRowaDBw9GpVKh0+nw8vIynePq6opOpwNAo/l72qdGo+HKlSskJydjb29vWrH/+Pl5CQ8PJzw8HIDg4GDc3NxMrykUilzPLUlpaNudOzB6tIJ9B4zU+t9G5K3msiHxLC8kv8Ac/zmM8B2Bk41Tnu8tDe17XqJtpZelt++fzJZQ2rdvT58+fQAICwtj3bp1jB49GknKu+RFXsefZ/A1MDCQwMBA0/PH77u7ublZ7H34ktw2SYKwMDs+nq0krc5K1NPnEcNfeMo8md96Pv/z/B82VjZkJmfyIDnvNpTk9v1Xom2ll6W2r1KlSnkeN1tCcXFxMT0OCAjg888/B3J6Ho9XONbpdLi6ugLkOq7ValGr1Tg6OpKamkp2djZWVla5zhdKvjt35Lw/JZtfs75C8eZXGKwfUKP8S7z94gxeqfoKcpkZt1IUBOGZmO1va0JCgunxiRMnTGta/Pz8OHbsGFlZWcTFxXHv3j08PT2pWbMm9+7dIy4uDoPBwLFjx/Dz80Mmk+Ht7c3x48cBOHLkCH5+fmZpk1BwRqPE3A2XaTnnE35tUgv8Z9C2pg87uu1gV/dddKjWQSQTQShliqWHsmjRIqKjo0lOTubNN9/k1VdfJSoqihs3biCTyShXrhwjR44EoEqVKjRv3pxx48Yhl8t54403kP//ht/Dhg1j9uzZGI1G/P39TUkoKCiIRYsWsWnTJqpXry7qi5VgD9Ie8O2p7ayM3EqKQxSyBjZ0rtKDD5qPoo5rHXOHJwjCfyCT8hu0KCPu3r1remyp9zvBvG3LMmZx6OYhwi6HEX7zMEYMyO40pYt7P4Jf64jazvk/X0N8d6WTJbcNLLd9JW4MRbB80dpowmLC2HF1B9p0LTaZFTCeGEfdzIGs+LQiNWtmmztEQRAKkUgoQqHSpevYeXUnYTFhXNBeQClX0sC6Exnfv0FGVCemjk9j1Cg9VlYimQiCpREJRfjPDEYDR24fISwmjIN/HSTLmEUDtwZM8fmUM+sHs2dbFRo2zGTR3gRq1zY8/QMFQSiVREIRnltMQgybYzaz7co24tLi0NhqGFJvCK/WepU7p3yZONIFnU7O+PFJvPOOHqUo/isIFk0kFOGZPMx4yPd/fs/mmM2cjj+NQqYgwCOAfrX64V/Fn/QUG2bMcGbzZnvq1s17b3dBECyTSCjCU2Ubs/n5zs9svrKZfTf2kZGdQV3XusxoNoNenr1ws8spLXH0qA0ffOBCXJycd99N5v33k3Pt7S4IgmUTCUXI158P/2TLlS1svbKVeyn3cLFxYWDtgfSr3Y/6mvqm0jd6vYxZs5wIDXXA0zOLXbt0+PpmmTl6QRCKm0goQi66dB37buxjc8xmIu9HIpfJ8a/sz8fNPuaVqq9gY2WT6/zffrNm3DgXbt2yYtQoPRMmJGFnZ6bgBUEwK5FQyriM7Az+uP8HR+8c5efbP3PuwTkkJDxdPJnaZCq9vXrnWSo+LU3GZ585smaNimrVDGzfrqVJk0wztEAQhJJCJJQyRpIkrjy8wtHbR4m4E8Fv934jzZCGQqagUflGfPDSB7Sr0o6Gbg3zreZ88qSSsWPVXL+uYOhQPVOmJGNvX6YLLgiCgEgoZYI2TcuhqEP8cOkHIu5EEJsSC0AN5xr0r9Wf1pVb0/yF5jhaOz7xc9LTYf58R5YvV1GpUjZhYQ9o1Ur0SgRByCESigVKN6QTeT+SiNsRRNyJ4IL2AgAuNi60qtSKNpXb0Nq9NZUdKxf4M8+dU/Leey7ExCgJCkph2rQkHB1Fr0QQhL+JhGIBJEniUsIlUwI5fu846dnpKOVK/Cr4MclvEt29u1NFUQUrudUzfXZmJnz5pSNffaWiXDkj69dradcuo4haIghCaSYSSikVlxrHz3d+5ujto/xy9xfup94HwMvFi6A6QabbWA5KB+D5qp5GRysYO1ZNVJSSPn1SmTkzERcX0SsRBCFvIqGUEmmGNE7EniDiTgRHbx/lou4iAK62rrzs/jJt3NvwsvvLVFLlXVb6WRgMsHSpioULHXF2NvLNNzo6dEj/z58rCIJlEwmlBJMkiYg7Eaw6v4pj946RkZ2BtdyaxhUbM7nxZNpUboO3xrvQdjY0GuH4cWtmz3bizBlrunVLY86cRFxdjYXy+YIgWDaRUEogSZL46fZPLDi1gNNxp3nB4QUG1x1Mm8ptaFqxKfZK+0K93vXrVmzdas+2bXbcuqVArc7m6691dO8ueiWCIBScSCgliCRJhN8MZ9HpRZyJP0NlVWU+b/U5fWv1/dcK9f8qKUnG7t12bNliR2SkDTKZxMsv2rll3gAAES9JREFUZzBxYjKdOqVjZyfGSgRBeDYioZQAkiRx8OZBFp5ayLkH56iiqsLcl+fSx6sP1laFV10xOxsiImzYssWO/fvtSE+X4emZxeTJSfTqlUqlSuLWliAIz08kFDMySkb239jPwtMLidJGUc2pGgtaL6CXVy+U8sLbPOTyZQXz51uxYUMF7t+3wsXFSL9+qfTtm4qPTxb5LIgXBEF4JiKhmIFRMrL3xl4WnlrIRd1FqjlVY2GbhfTy7IVCXjhfiU4nZ+fOnFta585Zo1BI+Ptn0LdvIoGB6fxfe/cfFHW1/3H8uQuCi4u4P/haYcb1B11JCRrMvpoIV+02ZV4zccbG2/UHo4XFjNYYzjjmdIe0Ggb1BqOZw22cqSa9SN3mNt0xE9C0QfnhVUQQSPOqoO7yY4Vl3d3z/WO/7JWSrtTKLuv7McPM7uGz6+flWXjzOefz+Zxw346gCSGEFJSB5FZuvmj8gq2VW6m11jImagzbUrfxh7F/8EkhcTjgwIGh7Nmj4+uvh3LjhoaHHrrBxo1tLF+uQ6u1+CCFEELcmhSUAeByu/ii6Qu2VGyhrrWOcSPG8V7ae8wdM7ffV67/mFLwr38NYc8eHcXFOiyWEKKjXSxdep309E7i4z2rJZrNOvp5XaMQQvSLFJQ7yOV28VnjZ2yt3MrZ1rPEjYij4HcFzPnNnF9dSJqbtRQV6dizJ4IzZ4YQFqZ44gk76emdpKZ2Eyo9K4QYYPJr5w5wup0UNxSztXIrjW2N/NbwW7bP3M7Tv3n6V12E2NUF//znUPbsiaCkJBy3W8MjjzjYtKmVuXO75LYoQgi/koLiQ063k6KzRWyt3Mr37d8Tb4xn56ydPBn75C8uJC4XHD8ext69Ov7+dx3t7Vruu8/JqlU2FizoZNw4l49TCCHELyMFxQduuG/wt/q/sa1yG+c6zjHRNJFds3fxxANP9LuQKOW5cr20NJxDh8L59ttw2tq06HRunnrKM6Q1bZoDrW/utiKEED4jBeVXcLgc7K3fy1+q/sL5jvMkmBMo/N9CZo+e3edqh7dy9aqWQ4fCKSsLo6wsnH//29Mto0Y5efrpLh5/vJuZM7vR62VISwgRuKSg/ALdrm4+rfuU96re44LtAonRifx56p+Zef/M2yoknZ0avvvOUzzKysKpqfFcxDhihJupU7t5+WUbKSndPPCASy46FEIMGlJQ+snldvH7ot9T31rPI//zCJsf30zqqNSfLSROp2fFw54Ccvx4GA6HhrAwxeTJDrKz20lJ6WbixBuE/LqTv4QQwm+koPRTiDaEjIkZ3B95PykxKbcsJEpBY2MIZWWeeZDDh8Npb/dMekyc6GD58utMn97No4865CaMQoigMSAFpaCggIqKCqKiosjNzQXAZrORl5fHlStXiI6OZvXq1ej1epRSFBYWUllZSXh4OJmZmYwZMwaAgwcPUlRUBMD8+fNJTU0FoLGxkfz8fBwOB0lJSSxdurRfcxj9tXjC4p+0Xbmi5fDhcEpLPXMhFy/+Zx5kzhzPPMjjjzswmeQGjEKI4DQgBSU1NZUnn3yS/Px8b1txcTGTJk1i3rx5FBcXU1xczOLFi6msrOTy5cts27aN+vp6PvjgA9566y1sNht79+5l8+bNAGRnZ5OcnIxer2fnzp2sXLmS8ePHs2nTJqqqqkhKSrqjmXrmQTwFJJzTp3vPg2Rl2Zg+XeZBhBB3jwE5+TQ+Ph69Xt+rrby8nBkzZgAwY8YMysvLATh27BgpKZ6hpLi4OK5fv47VaqWqqoqEhAT0ej16vZ6EhASqqqqwWq10dXURFxeHRqMhJSXF+153yp/+ZCQ+/h4WLzbx178Ow2h0s25dO//4xxVOnLjMzp1W/vjHTmJjpZgIIe4efptDaWtrw2AwAGAwGGhvbwfAYrFgNpu925lMJiwWCxaLBZPJ5G03Go23bO/Zvi/79+9n//79AGzevLnXvxUaGtrreV8mTAghIcHNzJlupk5VRERogKH//xWYbjfbYBXM+STb4BXs+X4s4CbllfrpJHVf8yEajeaW2/+cWbNmMWvWLO/zqzfdMdFsNvd63pfs7P887uz0fAW62802WAVzPsk2eAVrvvvuu++W7X673joqKgqr1QqA1Wpl+PDhgOcI4+YOuHbtGgaDAaPRyLVr17ztFosFg8GAyWTq1X7t2jWMRuMApRBCCNHDbwUlOTmZkpISAEpKSpg8ebK3vbS0FKUUdXV1REREYDAYSExMpLq6GpvNhs1mo7q6msTERAwGAzqdjrq6OpRSlJaWkpyc7K9YQghx1xqQIa8tW7ZQU1NDR0cHL774IgsXLmTevHnk5eVx4MABzGYza9asASApKYmKigqysrIICwsjMzMTAL1ez3PPPce6desAWLBggXeiPyMjg4KCAhwOB4mJiXf8DC8hhBA/pVH9nYQIMhcvXvQ+DtbxTgjubBDc+STb4BWs+QJuDkUIIURwkYIihBDCJ6SgCCGE8AkpKEIIIXzirp+UF0II4RtyhHKT7JsvgQ8ywZwNgjufZBu8gj3fj0lBEUII4RNSUIQQQvhEyMaNGzf6eycCSc9iXsEomLNBcOeTbINXsOe7mUzKCyGE8AkZ8hJCCOETUlCEEEL4RMAtsOVLBQUFVFRUEBUVRW5uLgDff/89O3fuxG63Ex0dTVZWFhEREbS0tLB69WrvTc/Gjx/PihUrAGhsbCQ/Px+Hw0FSUhJLly7tc9GvgdSffADnzp3j/fffp6urC41Gw6ZNmwgLCwvIfP3JVlZWxueff+597fnz53n77beJjY0d9NmcTifbt2+nqakJt9tNSkoKzz77LABVVVUUFhbidruZOXMm8+bN82csr/7me//992loaECr1bJkyRIeeughIDB/7q5evUp+fj6tra1oNBpmzZrFU089hc1mIy8vjytXrhAdHc3q1avR6/UopSgsLKSyspLw8HAyMzO9cyoHDx6kqKgIgPnz55OamurHZD6igtipU6dUQ0ODWrNmjbctOztbnTp1Siml1Ndff60+/vhjpZRSzc3Nvba7WXZ2tjpz5oxyu90qJydHVVRU3Pmdvw39yed0OtWrr76qmpqalFJKtbe3K5fL5X1NoOXrT7abnTt3Tq1atarXawZztrKyMpWXl6eUUsput6vMzEzV3NysXC6Xevnll9Xly5fVjRs31GuvvaZ++OGHgQ9zC/3J9+WXX6r8/HyllFKtra1q7dq1Af25tFgsqqGhQSmlVGdnp8rKylI//PCD2r17t9q3b59SSql9+/ap3bt3K6WUOn78uMrJyVFut1udOXNGrVu3TimlVEdHh1q1apXq6Ojo9XiwC+ohr/j4eO+aKT0uXrzIhAkTAEhISOC777772fewWq10dXURFxeHRqMhJSWF8vLyO7bP/dGffNXV1YwePZrY2FgAIiMj0Wq1AZvvl/bdoUOHmDZtGhC4fdffbHa7HZfLhcPhIDQ0lIiICM6ePcs999zDyJEjCQ0NZerUqQGRDfqX78KFC0ycOBHwrOI6bNgwGhsbA7bvDAaD9whDp9MRExODxWKhvLycGTNmADBjxgzvvh47doyUlBQ0Gg1xcXFcv34dq9VKVVUVCQkJ6PV69Ho9CQkJVFVV+S2XrwR1QbmV+++/n2PHjgFw9OjRXssHt7S0sHbtWt544w1Onz4NeJYaNplM3m1MJhMWi2Vgd7of+sp36dIlNBoNOTk5vP7663z22WfA4Mr3c33X48iRI96CEgzZHnvsMYYOHcqKFSvIzMzkmWeeQa/XD6ps0He+2NhYjh07hsvloqWlhcbGRq5evToo8rW0tNDU1MS4ceNoa2vDYDAAnqLT3t4OeD6DZrPZ+5qeHD/OZzQaAy7fLxHUcyi38tJLL1FYWMjevXtJTk4mNNTzX2AwGCgoKCAyMpLGxkbeffddcnNzUYPsrOq+8rlcLmpra9m0aRPh4eG8+eabjBkzBp1O5+c9vn19ZetRX19PWFgYo0ePBhhUfddXtrNnz6LVatmxYwfXr19nw4YNTJo06ZbZ/D2/8HP6ypeWlsaFCxfIzs4mOjqaBx98kJCQkIDvO7vdTm5uLkuWLPHOUd5Kf/opkPvvdt11BSUmJob169cDnsPwiooKAIYMGcKQIUMAz4VII0eO5NKlS5hMpl5/CV+7dg2j0TjwO36b+spnMpmIj49n+PDhgGep5aamJqZPnz5o8vWVrcfhw4e9RyfAoOq7vrIdOnSIxMREQkNDiYqK4sEHH6ShoQGz2fyTbD1/IQeivvKFhISwZMkS73br16/n3nvvZdiwYQHbd06nk9zcXKZPn86UKVMAz3Cd1WrFYDBgtVq9P2cmk6nXio09/WQ0GqmpqfG2WywW4uPjBzbIHXDXDXm1tbUB4Ha7KSoqYvbs2QC0t7fjdrsBaG5u5tKlS4wcORKDwYBOp6Ourg6lFKWlpSQnJ/tt//+bvvI9/PDDnD9/nu7ublwuF6dPn2bUqFGDKl9f2Xrajh492qugBEM2s9nMyZMnUUpht9upr68nJiaGsWPHcunSJVpaWnA6nXz77bcBmw36ztfd3Y3dbgfgxIkThISEBPTnUinF9u3biYmJYc6cOd725ORkSkpKACgpKWHy5Mne9tLSUpRS1NXVERERgcFgIDExkerqamw2GzabjerqahITE/2SyZeC+kr5LVu2UFNTQ0dHB1FRUSxcuBC73c5XX30FwKOPPsrzzz+PRqPh6NGjfPrpp4SEhKDVaklPT/d+gBsaGigoKMDhcJCYmMiyZcsC4vC0P/kASktLKS4uRqPRkJSUxOLFi4HAzNffbKdOneKjjz4iJyen1/sM9mx2u52CggIuXLiAUoq0tDTmzp0LQEVFBR9++CFut5u0tDTmz5/vz1he/cnX0tJCTk4OWq0Wo9HIiy++SHR0NBCYfVdbW8uGDRsYPXq0d18WLVrE+PHjycvL4+rVq5jNZtasWeM9bXjXrl1UV1cTFhZGZmYmY8eOBeDAgQPs27cP8Jw2nJaW5rdcvhLUBUUIIcTAueuGvIQQQtwZUlCEEEL4hBQUIYQQPiEFRQghhE9IQRFCCOETUlCEEEL4hBQUIe6wbdu2UVBQ0KutpqaGZcuWYbVa/bRXQvieFBQh7rClS5dSWVnJiRMnAHA4HOzYsYMXXnjBp7dL6bnTgxD+ctfdy0uIgRYZGcmyZcvYsWMHubm5FBUVMXLkSFJTU3G73RQXF/PNN9/Q2dnJpEmTyMjIQK/X43a7ycvLo7a2lhs3bhAbG0tGRgajRo0CPEc+ERERNDc3U1tbS3Z2tndxKiH8Qa6UF2KA5Obm4nQ6OXPmDO+88w5ms5nPP/+c8vJy7wp/u3btwul08sorr+B2uyktLWXKlCmEhISwe/du6uvr2bx5M+ApKJWVlaxbt45x48bhcrm8NzgVwh9kyEuIAbJ8+XJOnjzJggULvGtk7N+/n0WLFmE0GgkLCyM9PZ0jR47gdrvRarWkpqai0+m832tsbPTeTBFg8uTJxMXFodVqpZgIv5MhLyEGyIgRIxg+fLh3yAo8a5S//fbbvW56qNFoaG9vZ/jw4Xz00UccPXqUjo4O7zYdHR0MHToUoNfiTUL4mxQUIfzIZDKRlZXF+PHjf/K9b775hsrKSjZs2EB0dDQdHR1kZGQE/OJT4u4lQ15C+NHs2bP5+OOPvYswtbW1eZfK7erqIjQ0lMjISLq7u/nkk0/8uatC/FdyhCKEH/Us0vTmm2/S2tpKVFQU06ZNIzk5mbS0NE6cOMHKlSuJjIwkPT2d/fv3+3mPheibnOUlhBDCJ2TISwghhE9IQRFCCOETUlCEEEL4hBQUIYQQPiEFRQghhE9IQRFCCOETUlCEEEL4hBQUIYQQPvF/Vw4L0F3HvTYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select two countries' worth of data.\n",
    "gdp_australia = data.loc['Australia']\n",
    "gdp_nz = data.loc['New Zealand']\n",
    "\n",
    "# Plot with differently-colored markers, add x- and y-axis labels.\n",
    "plt.plot(years, gdp_australia, 'b-', label='Australia')\n",
    "plt.plot(years, gdp_nz, 'g-', label='New Zealand')\n",
    "\n",
    "# Create legend.\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP per capita ($)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to line graphs, matplotlib can produce many other types and styles of graph, such as a scatter plot. To make one all we have to do is use the <code>.scatter()</code> function in much the same way as we used the <code>.plot()</code> function. In fact, even though we are currently working in a different code block, the variables that we've created up until this point are still valid and accessible. Let's use the same variables as before to populate our scatter plot's x- and y-axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc6211bd910>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gdp_australia, gdp_nz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Programming languages like Python more often than not allow for multiple different solutions to the same problem. As a small example, we'll use a different method for labeling our axes. <blockquote><i>Notice that with this code block, we are writing all of our code on just a single line using method chaining.</blockquote></i>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc610ccdbd0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.T.plot.scatter(x = 'Australia', y = 'New Zealand')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many functions in Python can be chained together using what's called a method chain. Method chaining slims down your code, not only saving you space but also often making your code easier for other's read and understand. Let's import some new data and plot a graph using method chaining and the <code>.min()</code> and <code>.max()</code> functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.,  2.,  4.,  6.,  8., 10., 12.]),\n",
       " <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# changing to tab here too\n",
    "data_europe = pd.read_csv('data/gapminder_gdp_europe.tab', index_col='country', sep='\\t')\n",
    "data_europe.min().plot(label='min')\n",
    "data_europe.max().plot(label='max')\n",
    "plt.legend(loc='best')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing data like this can help researchers make quick, broad analyses of a given dataset, thereby helping to reveal interesting relationships in the data. Such analyses also often reveal new lines of inquiry to scholars. For example, what (if any) relationship do you see between the minimum and maximum GDP per capita data among Asian countries for each year in the dataset? We can produce a scatter plot to help us answer this question:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc610ac7e10>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_asia = pd.read_csv('data/gapminder_gdp_asia.tab', index_col='country', sep='\\t')\n",
    "data_asia.describe().T.plot(kind='scatter', x='min', y='max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears as though there are no particular relationships, nor any obvious correlations between the min and max GDP values year by year. The fortunes of Asian countries, according to this data, do not rise and fall together. However, the graph does show us that there is a lot more variability among the maximum values than the minimum values. To investigate, let's take a look at those max values and their corresponding index. (Recall that we set our index to be the unique values in the 'country' column.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/version/0.15.2/generated/pandas.DataFrame.plot.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6607b0ed0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_asia = pd.read_csv('data/gapminder_gdp_asia.tab', index_col='country', sep='\\t')\n",
    "data_asia.max().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right away you'll notice our labels (x-ticks) are illegible. The `.plot()`, like other functions in Python, accepts different parameters. That is to say, we can modify the output of our function, or alternatively control how the functions inputs or interprets information. For example, when we read our csv file we've been using the `index_col` parameter to set our index values. We will use the `rot` parameter to rotate our x-ticks 45 degrees so that they are legible. As you may have guessed, the `rot` parameter takes an integer as its argument, or input. For a full list of parameters and what they do, see this documentation:\n",
    "\n",
    "<blockquote><i>\n",
    "    https://pandas.pydata.org/pandas-docs/version/0.15.2/generated/pandas.DataFrame.plot.html\n",
    "</blockquote></i>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc670de8bd0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_asia.max().plot(rot = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph reveals a precipitous drop after 1972, possibly alluding to some kind of global, geopolitical event.\n",
    "\n",
    "Our Gapminder datasets also includes life expectancy data. The following short code block will show the correlation between a country's life expectancy and its GDP for 2007, normalizing marker size by population using the scatter plot's s argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc670e54d50>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_all = pd.read_csv('data/gapminder_all_data.tab', index_col='country', sep='\\t')\n",
    "data_all.plot(kind='scatter', x='gdpPercap_2007', y='lifeExp_2007',\n",
    "              s=data_all['pop_2007']/1e6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, let's download some of these graphs by using the <code>.savefig()</code> function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('my_figure.png') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, your graph will be saved as a PNG file called \"my_figure\" and will be saved to your current working directory. If you are unsure of your current working directory, you can simply type \"pwd\", which stands for Print Working Directory, and then run it and Python will print your current working directory. You can change the file format by changing the file extension to one of the other available formats: pdf, ps, eps, and svg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/dougdaniels/Documents/GitHub/is262b_final'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is often the case that when you work with dataframes, your data is generated and plotted to screen in a single line (method chaining), and the plt.savefig() function doesn't seem viable. A possible workaround is to assign a reference to your figure to a local variable using plt.gcf(), and to then use the .savefig() function on your variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.gcf() # get current figure\n",
    "data.plot(kind='bar')\n",
    "fig.savefig('my_figure.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. <span id=\"fn1\">While working on this project we found a bug in how mybinder works with the UCLA Dataverse instance  . We are working with the mybinder group to identify and fix the bug. For this project, we used the Harvard Dataverse instance.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
